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A data-driven approach to the automated study of cross-species homologies

Pauli, Wolfgang M. (2018) A data-driven approach to the automated study of cross-species homologies. . (Unpublished)

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Behavioral neuroscience has made great strides in developing animal models of human behavior and psychiatric disorders. Animal models allow for the formulation of hypotheses regarding the mechanisms underlying psychiatric disorders, and the opportunity to test these hypotheses using procedures that are too invasive for human participants. However, recent scientific reviews have highlighted the low success rate of translating results from animal models into clinical interventions in humans. A potential roadblock is that bidirectional functional mappings between the human and rodent brain are incomplete. To narrow this gap, we created a framework, Neurobabel, for performing large-scale automated synthesis of human neuroimaging data and behavioral neuroscience data. By leveraging the semantics of how researchers within each field describe their studies, this framework enables region to region mapping of brain regions across species, as well as cross-species mapping of psychological functions. As a proof of concept, we utilize the framework to create a functional cross-species mapping between the amygdala and hippocampus for fear-related and spatial memories, respectively. We then proceed to address two open questions in the field: (1) Do rodents have a dorsolateral prefrontal cortex? (2) Which human brain region corresponds to the rodent prelimbic cortex?

Item Type:Report or Paper (Discussion Paper)
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Pauli, Wolfgang M.0000-0002-0966-0254
Alternate Title:A data-driven approach to the automated mapping of functional brain topographies across species
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. Thank you to Dr. Jane E. Barker and Dr. Julian M. Tyszka for constructive discussions of the approach, and for proofreading the manuscript. Further thanks go to Dr. Tal Yarkoni for opening up the source code for neurosynth to the public. Code Availability: The source code for creating the dataset and performing the analyses reported here are available as a github repository: Data Availability: The above repository also includes the present release of the dataset.
Record Number:CaltechAUTHORS:20180927-114225248
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:90013
Deposited By: George Porter
Deposited On:27 Sep 2018 20:28
Last Modified:16 Nov 2021 00:40

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